!! New Framework Released for Satellite Image Classification !!
satellighte: PyTorch Lightning Implementations of Recent Satellite Image Classification !
TABLE OF CONTENTS
EuroSAT is a large-scale land use and land cover classification dataset derived from multispectral Sentinel-2 satellite imagery covering European continent. EuroSAT is composed of 27,000 georeferenced image patches (64 x 64 pixels) - each patch comprises 13 spectral bands (optical through to shortwave infrared ) resampled to 10m spatila resolution and labelled with one of 10 distinct land cover classes: AnnualCrop, Forest, HerbaceousVegetation, Highway, Industrial, Pasture, PermanentCrop, Residential, River, SeaLake. Full details including links to journal papers and download instructions may be found here: https://github.com/phelber/eurosat.
Source: eurosat-github-page
This project is licensed under MIT license. See LICENSE for more information.
The references used in the development of the project are as follows.
@article{helber2019eurosat,
title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2019},
publisher={IEEE}
}@inproceedings{helber2018introducing,
title={Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
pages={204--207},
year={2018},
organization={IEEE}
}Give a ⭐️ if this project helped you!
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